ML Model Card Generator

A model card is a standardized documentation file (README.md) that describes a machine learning model — its intended use, training data, performance metrics, limitations, and ethical considerations. HuggingFace uses model cards to populate the model hub page and power search/filtering. The YAML frontmatter (model-index) is machine-readable and used to display metrics on the leaderboard. Well-documented models are more likely to be trusted and adopted by the community.

Describe the specific tasks your model is designed for, the expected input format, and the target user group. Be concrete: instead of 'NLP tasks', write 'Binary sentiment classification of English product reviews, returning positive/negative with confidence scores'. Also explicitly state out-of-scope uses — this protects users from misapplying the model and reduces liability. Example out-of-scope: 'Not intended for medical diagnosis, legal analysis, or non-English text.'

Include metrics relevant to your task: accuracy and F1 for classification, ROUGE/BLEU for generation, RMSE/MAE for regression. Always specify the dataset the metrics were measured on — separate train/validation/test set metrics. For classification, include per-class metrics if there's class imbalance. The HuggingFace YAML format (model-index) makes these metrics appear in the model hub UI and enables comparison across models.

Document known biases in training data (demographic, geographic, temporal), potential for harmful outputs or misuse, fairness across demographic groups (if applicable), and any privacy considerations. For models trained on web data, note the possibility of reproducing harmful stereotypes. Include a disclaimer if the model should not be used for high-stakes decisions (healthcare, hiring, legal) without human review. Transparency here builds trust and enables safe deployment.